Clustering of machining signal for verifying machining parameter

  • Authors:

  • M. Z. Nuawi;F. Lamin;M. J. M. Nor;N. Jamaluddin;S. Abdullah

  • Affiliations:

  • Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Materials Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

  • Venue:
  • SENSIG'08 Proceedings of the 1st WSEAS international conference on Sensors and signals
  • Year:
  • 2008

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Abstract

Dynamic state recognition and event-prediction are fundamental tasks in signal processing. This paper presents a novel identification method which could form the basis for forecasting a generalized machining condition. The method relies on the value of I-kaz coefficient, which is an extractable unique feature that can be gained for every signal acquired during the cutting process. The method is useful for classifying the acquired signal in the machining process to a set of cluster which may represent the specific cutting condition of the machining process. The classification method was succeeded in identifying the cutting parameter that being used to generate the signal, which was the combination of the cutting speed, feed rate and depth of cut. This kind of clustering is very useful in the analysis of machining signal processing such as signal conformation, fault identification and etc.